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We present our NVIDIA NCA-GENM real questions in PDF format. It is beneficial for those applicants who are busy in daily routines. The NCA-GENM PDF QUESTIONS contains all the exam questions which will appear in the real test. You can easily get ready for the examination in a short time by just memorizing NCA-GENM Actual Questions.

NVIDIA Generative AI Multimodal Sample Questions (Q213-Q218):

NEW QUESTION # 213
Which statistical method is most appropriate for evaluating the agreement between multiple human annotators labeling images with severity scores on a scale of 1 to 5, for a multimodal medical imaging application?

A. Krippendorff's Alpha
B. Chi-squared test
C. Pearson correlation coefficient
D. Cohen's Kappa
E. Spearman's rank correlation coefficient

Answer: A

Explanation:
Krippendorff's Alpha is the most appropriate measure when dealing with multiple annotators, ordinal data (severity scores on a scale), and potential for missing data. While Cohen's Kappa is suitable for two annotators, Krippendorff's Alpha generalizes to multiple annotators. Spearman's rank correlation is better suited for assessing the monotonic relationship between variables, but not agreement among raters. Pearson correlation requires interval data, while chi-squared is for categorical.

NEW QUESTION # 214
You're designing a multimodal A1 system for autonomous driving that integrates data from cameras (images), LiDAR (point clouds), radar (time-series), and GPS (geospatial). The system needs to make real-time decisions in complex urban environments. Which hardware and software components are crucial for achieving low latency and high accuracy in data processing and fusion?

A. Sensor fusion algorithms optimized for GPU acceleration.
B. High-bandwidth, low-latency communication interfaces (e.g., PCle Gen4/5) for data transfer between sensors and processing units.
C. NVIDIA GPUs with CUDA for accelerated processing of image and point cloud data.
D. Real-time operating system (RTOS) for deterministic execution and minimal jitter.
E. All of the above.

Answer: E

Explanation:
All the options are critical for real-time performance. GPUs accelerate processing, high-bandwidth interfaces enable fast data transfer, RTOS ensures deterministic execution, and optimized sensor fusion algorithms maximize accuracy.

NEW QUESTION # 215
A financial institution aims to detect fraudulent transactions by analyzing transaction history (time-series), customer profiles (text and numerical data), and network activity (graph data). The system must identify fraudulent patterns in real-time. Which of the following architectural patterns is MOST suitable for building this multimodal fraud detection system, considering both accuracy and latency requirements?

A. A hybrid approach that combines real-time stream processing for initial detection and batch processing for periodic model retraining and refinement.
B. A real-time stream processing architecture that uses a combination of rule-based systems and machine learning models to detect fraudulent transactions as they occur.
C. A batch processing pipeline that aggregates data daily and trains a machine learning model to identify fraudulent transactions.
D. Train three seperate models for each data set, transaction history, customer profile, and network activitiy.
E. An architecture based solely on rule-based systems to minimize latency, ignoring the potential for machine learning to detect complex fraud patterns.

Answer: A

Explanation:
A hybrid approach is best. Real-time stream processing enables immediate detection, while batch processing allows for more sophisticated analysis and model updates, improving accuracy over time. Batch processing alone is too slow for real-time fraud detection, and rule- based systems are less adaptable to evolving fraud patterns.

NEW QUESTION # 216
You are training a variational autoencoder (VAE) for generating images. The generated images are blurry and lack fine details. Which of the following modifications to the VAE architecture or training procedure would be MOST effective in improving the image quality?

A. Using a deeper latent space (higher dimensionality).
B. Increasing the capacity of the encoder and decoder networks.
C. Using a smaller batch size.
D. Decreasing the value of the KL divergence weight in the loss function.
E. Using a larger batch size.

Answer: D

Explanation:
Decreasing the KL divergence weight allows the model to focus more on reconstructing the input images and less on forcing the latent space to conform to a standard normal distribution. This can lead to sharper, more detailed images. Increasing the capacity of the networks or the dimensionality of the latent space may help, but only if the KL divergence weight is appropriately adjusted. Larger or smaller batch sizes do not directly influence the blurriness problem.

NEW QUESTION # 217
You are building a multimodal AI system that generates 3D models of furniture from text descriptions and a few 2D images of similar furniture pieces. The system uses separate encoders for text and images. You want to fuse the information from both modalities effectively. Which TWO of the following fusion techniques would be the most appropriate for this task, considering the different nature of the text and image data?

A. Applying element-wise addition of the text and image embeddings.
B. Using a cross-attention mechanism where the text embedding attends to the image features, and vice-versa, allowing the model to dynamically weight the importance of different parts of each modality.
C. Using a gating mechanism (e.g., a learned weight) to control the contribution of the text and image embeddings based on the input.
D. Performing a simple average of the text and image embeddings.
E. Simple concatenation of the text and image embeddings before feeding them into the decoder.

Answer: B,C

Explanation:
Cross-attention (C) allows the model to dynamically learn the relationships between the text and image, highlighting relevant features from each modality- A gating mechanism (D) provides a learned way to control the contribution of each modality allowing the model to prioritize the more informative input Simple concatenation (A), addition (B), or averaging (E) are less sophisticated and might not effectively capture the complex interactions between the modalities.

NEW QUESTION # 218
......

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